Decomposable negation normal form
Journal of the ACM (JACM)
Back to the Future for Consistency-Based Trajectory Tracking
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Model-Based Programming: Controlling Embedded Systems by Reasoning About Hidden State
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Timed model-based programming: executable specifications for robust mission-critical sequences
Timed model-based programming: executable specifications for robust mission-critical sequences
Diagnosis as approximate belief state enumeration for probabilistic concurrent constraint automata
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Model compilation for real-time planning and diagnosis with feedback
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Efficient Genome Wide Tagging by Reduction to SAT
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
New compilation languages based on structured decomposability
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
Algorithms for generating ordered solutions for explicit and/or structures
Journal of Artificial Intelligence Research
| Hi-index | 0.00 |
As embedded systems grow increasingly complex, there is a pressing need for diagnosing and monitoring capabilities that estimate the system state robustly. This paper is based on approaches that address the problem of robustness by reasoning over declarative models of the physical plant, represented as a variant of factored Hidden Markov Models, called Probabilistic Concurrent Constraint Automata. Prior work on Mode Estimation of PCCAs is based on a Best-First Trajectory Enumeration (BFTE) algorithm. Two algorithms have since made improvements to the BFTE algorithm: 1) the Best-First Belief State Update (BFBSU) algorithm has improved the accuracy of BFTE and 2) the MEXEC algorithm has introduced a polynomial-time bounded algorithm using a smooth deterministic decomposable negation normal form (sd-DNNF) representation. This paper introduces a new DNNF-based Belief State Estimation (DBSE) algorithm that merges the polynomial time bound of the MEXEC algorithm with the accuracy of the BFBSU algorithm. This paper also presents an encoding of a PCCA as a CNF with probabilistic data, suitable for compilation into an sd-DNNF-based representation. The sd-DNNF representation supports computing k belief states from k previous belief states in the DBSE algorithm.